National and Subnational estimates for the United States of America

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally in the United States of America. These results are impacted by changes in testing effort, increases and decreases in testing effort will increase and decrease reproduction number estimates respectively (see Methods for further explanation).

Table of Contents


Using data available up to the: 2020-04-12

Note that it takes time for infection to cause symptoms, to get tested for SARS-CoV-2 infection, for a positive test to return and ultimately to enter the case data presented here. In other words, today’s case data are only informative of new infections about two weeks ago. This is reflected in the plots below, which are by date of infection.

Expected daily confirmed cases by region


Figure 1: The results of the latest reproduction number estimates (based on estimated confirmed cases with a date of infection on the 2020-04-01) in the United States of America, stratified by state, can be summarised by whether confirmed cases are likely increasing or decreasing. This represents the strength of the evidence that the reproduction number in each region is greater than or less than 1, respectively (see the methods for details). Regions with fewer than 40 confirmed cases reported on a single day are not included in the analysis (light grey).

National summary

Summary (estimates as of the 2020-04-01)

Table 1: Latest estimates (as of the 2020-04-01) of the number of confirmed cases by date of infection, the expected change in daily confirmed cases, the effective reproduction number, the doubling time, and the adjusted R-squared of the exponential fit. The mean and 90% credible interval is shown for each numeric estimate.
Estimate
New confirmed cases by infection date 33561 (32887 – 34205)
Expected change in daily cases Increasing
Effective reproduction no. 1.1 (1.1 – 1.2)
Doubling time (days) 55 (42 – 78)
Adjusted R-squared 0.88 (0.79 – 0.98)

Reported confirmed cases, their estimated date of infection, and time-varying reproduction number estimates


Figure 2: A.) Confirmed cases by date of report (bars) and their estimated date of infection. B.) Time-varying estimate of the effective reproduction number. Light ribbon = 90% credible interval; dark ribbon = the 50% credible interval. Estimates are shown until the 2020-04-01.Dark grey ribbon = 50% credible interval. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.

Time-varying rate of growth and doubling time


Figure 3: A.) Time-varying estimate of the rate of growth, B.) Time-varying estimate of the doubling time in days (note that when the rate of growth is negative the doubling time is assumed to be infinite), C.) The adjusted R-squared estimates indicating the goodness of fit of the exponential regression model (with values closer to 1 indicating a better fit). Estimates are shown until the 2020-04-01. Light ribbon = 90% credible interval; dark ribbon = the 50% credible interval. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.

Regional Breakdown

Data availability

Limitations

Summary of latest reproduction number and confirmed case count estimates by date of infection


Figure 4: Confirmed cases with date of infection on the 2020-04-01 and the time-varying estimate of the effective reproduction number (light bar = 90% credible interval; dark bar = the 50% credible interval.). Regions are ordered by the number of expected daily confirmed cases and shaded based on the expected change in daily confirmed cases. The dotted line indicates the target value of 1 for the effective reproduction no. required for control and a single case required for elimination.

Reproduction numbers over time in the six regions expected to have the most new confirmed cases


Figure 5: Time-varying estimate of the effective reproduction number (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in the regions expected to have the highest number of new confirmed cases. Estimates are shown up to the 2020-04-01. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The dotted line indicates the target value of 1 for the effective reproduction no. required for control.

Reported confirmed cases and their estimated date of infection in the six regions expected to have the most new confirmed cases


Figure 6: Confirmed cases by date of report (bars) and their estimated date of infection (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in the regions expected to have the highest number of new confirmed cases. Estimates are shown up to the 2020-04-01. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.

Reproduction numbers over time in all regions


Figure 7: Time-varying estimate of the effective reproduction number (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in all regions. Estimates are shown up to the 2020-04-01. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The dotted line indicates the target value of 1 for the effective reproduction no. required for control.

Reported confirmed cases and their estimated date of infection in all regions

Figure 8: Confirmed cases by date of report (bars) and their estimated date of infection (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in all regions. Estimates are shown up to the 2020-04-01. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.

Latest estimates (as of the 2020-04-01)

Table 2: Latest estimates (as of the 2020-04-01) of the number of confirmed cases by date of infection, the effective reproduction number, and the doubling time in each region. The mean and 90% credible interval is shown.
State New confirmed cases by infection date Expected change in daily cases Effective reproduction no. Doubling time (days)
Alabama 260 (183 – 327) Increasing 1.3 (1 – 1.5) 16 (7.9 – 780)
Alaska 179 (127 – 224) Likely increasing 1.2 (1 – 1.5) 18 (8.2 – Inf)
Arizona 192 (122 – 271) Unsure 1 (0.8 – 1.3) -310 (13 – Inf)
Arkansas 84 (42 – 129) Likely increasing 1.2 (0.9 – 1.6) 19 (5.8 – Inf)
California 1261 (1115 – 1390) Likely increasing 1 (1 – 1.1) -280 (47 – Inf)
Colorado 346 (271 – 413) Unsure 1 (0.9 – 1.1) -77 (27 – Inf)
Connecticut 929 (813 – 1042) Increasing 1.2 (1.1 – 1.3) 17 (11 – 36)
Delaware 142 (88 – 201) Increasing 1.3 (1 – 1.6) 13 (6 – Inf)
District of Columbia 148 (95 – 203) Likely increasing 1.2 (1 – 1.5) 18 (7 – Inf)
Florida 1097 (983 – 1223) Unsure 1 (0.9 – 1.1) -170 (48 – Inf)
Georgia 1002 (873 – 1114) Increasing 1.3 (1.1 – 1.4) 15 (10 – 28)
Guam 102 (52 – 158) Increasing 1.7 (1.2 – 2.3) 5.9 (3.3 – 25)
Hawaii 356 (290 – 416) Increasing 1.3 (1.1 – 1.5) 13 (7.9 – 33)
Idaho 77 (34 – 115) Unsure 0.9 (0.6 – 1.2) -14 (17 – Inf)
Illinois 1395 (1261 – 1536) Increasing 1.2 (1.1 – 1.3) 25 (15 – 64)
Indiana 486 (406 – 575) Unsure 1.1 (0.9 – 1.2) 98 (19 – Inf)
Iowa 121 (69 – 170) Likely increasing 1.3 (0.9 – 1.6) 16 (6.3 – Inf)
Kansas 99 (54 – 150) Likely increasing 1.2 (0.8 – 1.6) 21 (6.1 – Inf)
Kentucky 155 (100 – 199) Increasing 1.3 (1 – 1.6) 13 (6.3 – Inf)
Louisiana 1112 (994 – 1238) Unsure 0.9 (0.8 – 1.1) -18 (Inf – Inf)
Maine 36 (4 – 68) Unsure 1.1 (0.5 – 1.7) -87 (4.8 – Inf)
Maryland 753 (641 – 854) Increasing 1.4 (1.2 – 1.5) 11 (7.9 – 18)
Massachusetts 1819 (1653 – 1982) Increasing 1.2 (1.2 – 1.3) 16 (12 – 26)
Michigan 1346 (1205 – 1483) Unsure 1 (0.9 – 1.1) -42 (1500 – Inf)
Minnesota 98 (53 – 144) Likely increasing 1.2 (0.9 – 1.6) 15 (5.6 – Inf)
Mississippi 188 (124 – 239) Likely increasing 1.2 (1 – 1.4) 20 (8.2 – Inf)
Missouri 274 (204 – 333) Likely increasing 1.1 (0.9 – 1.3) 39 (12 – Inf)
Montana 292 (227 – 344) Increasing 1.3 (1.1 – 1.5) 14 (8.2 – 64)
Nebraska 73 (33 – 110) Likely increasing 1.4 (1 – 1.8) 11 (4.5 – Inf)
Nevada 166 (106 – 224) Unsure 1.1 (0.8 – 1.3) 51 (10 – Inf)
New Hampshire 54 (23 – 84) Unsure 1 (0.7 – 1.4) -44 (8.7 – Inf)
New Jersey 3410 (3178 – 3644) Unsure 1 (1 – 1.1) -90 (210 – Inf)
New Mexico 111 (64 – 161) Increasing 1.3 (1 – 1.7) 13 (5.5 – Inf)
New York 9951 (9569 – 10329) Increasing 1.1 (1 – 1.1) 120 (54 – Inf)
North Carolina 257 (194 – 318) Unsure 1.1 (0.9 – 1.2) 76 (14 – Inf)
North Dakota 202 (151 – 252) Increasing 1.3 (1.1 – 1.6) 12 (6.8 – 68)
Ohio 366 (287 – 434) Unsure 1 (0.9 – 1.2) -1000 (21 – Inf)
Oklahoma 124 (66 – 169) Unsure 1 (0.8 – 1.3) -140 (12 – Inf)
Oregon 75 (31 – 115) Unsure 1 (0.7 – 1.3) -88 (8.8 – Inf)
Pennsylvania 1777 (1613 – 1921) Increasing 1.2 (1.1 – 1.3) 24 (16 – 51)
Puerto Rico 65 (25 – 101) Unsure 1.2 (0.7 – 1.6) 29 (5.8 – Inf)
Rhode Island 245 (182 – 299) Increasing 1.5 (1.2 – 1.7) 8 (5.3 – 17)
South Carolina 218 (155 – 273) Likely increasing 1.1 (0.9 – 1.3) 47 (12 – Inf)
South Dakota 73 (33 – 107) Increasing 1.5 (1.1 – 2) 7.8 (3.8 – Inf)
Tennessee 263 (198 – 318) Unsure 1 (0.9 – 1.2) -310 (19 – Inf)
Texas 1068 (955 – 1201) Increasing 1.2 (1.1 – 1.3) 18 (12 – 38)
Utah 118 (73 – 165) Unsure 1 (0.7 – 1.2) -43 (14 – Inf)
Vermont 45 (11 – 73) Unsure 1.1 (0.6 – 1.5) -190 (6.3 – Inf)
Virgin Islands 50 (20 – 79) Likely increasing 1.4 (0.9 – 1.9) 12 (4.3 – Inf)
Virginia 401 (326 – 477) Increasing 1.2 (1.1 – 1.4) 19 (10 – 240)
Washington 389 (318 – 467) Likely decreasing 0.9 (0.8 – 1) -25 (210 – Inf)
West Virginia 71 (18 – 116) Likely increasing 1.5 (0.9 – 2) 9.6 (3.7 – Inf)
Wisconsin 166 (106 – 212) Unsure 1 (0.8 – 1.2) -89 (17 – Inf)
Wyoming 194 (145 – 244) Increasing 1.3 (1.1 – 1.6) 12 (6.6 – 59)

“2019 Novel Coronavirus Covid-19 (2019-nCoV) Data Repository.” 2020. Johns Hopkins CSSE. https://github.com/CSSEGISandData/COVID-19.

Abbott, Sam, Joel Hellewell, James D. Munday, and Sebastian Funk. 2020. “NCoVUtils: Utility Functions for the 2019-Ncov Outbreak.” - - (-): –. https://doi.org/10.5281/zenodo.3635417.

Xu, Bo, Bernardo Gutierrez, Sarah Hill, Samuel Scarpino, Alyssa Loskill, Jessie Wu, Kara Sewalk, et al. n.d. “Epidemiological Data from the nCoV-2019 Outbreak: Early Descriptions from Publicly Available Data.” http://virological.org/t/epidemiological-data-from-the-ncov-2019-outbreak-early-descriptions-from-publicly-available-data/337.

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